This prominent medical center is using Jvion's Acute Myocardial Infarction (AMI) clinical vectors to stop heart attacks are using Jvion's Acute Myocardial Infarction (AMI) clinical vectors to stop heart attacks. Jvion's solution is performing almost two times better than stress tests and 20% better than CT coronary angiograms in identifying AMI events in a low risk population within 12 months of discharge.
"Smack dab" in the middle of Mississippi sits this prominent academic medical center. Like most academic facilities, they treat some of the most complex cases and sickest patients. But in Mississippi things are even more challenging.
This provider treats patients who are more likely to smoke, be overweight, and to have had a stroke. They are also least likely to exercise or to have health coverage. Not surprisingly, heart disease is the leading cause of death in Mississippi.
The complexity of the patient population was always a challenge. One out of every 10 patients leaving the hospital suffered a heart attack within 12 months. “This has to stop” thought the system's Chief Health Information Officer (CHIO), “there simply has to be a better way. If we could just predict patients at risk of a heart attack within a year, we could better allocate resources and improve the health of Mississippians.”
So, the CHIO looked to Jvion. He knew that Jvion’s Cognitive Clinical Success Machine was extremely powerful and he knew the Jvion team. But could a vector be developed that would target Acute Myocardial Infarctions (AMIs)? And could we stand up the solution quickly?
They got to work. Jvion worked to fine tune the machine to 12-month AMI risk. The provider worked to validate the machine’s outputs, provide preferences, and deliver data support when needed.
After just a few months tuning the engine and integrating the machine’s outputs into Epic, the hospital can take the entire population of patients and zero in on a target group of 1.5%.
Within that group, 75 out of 100 patients are at risk of a heart attack within 12 months. Since the implementation of the solution, more than 10,000 patients have had more than 50,000 daily predictions automatically rendered through the EHR.
“This solution has helped us identify a previously unidentifiable population—patients at risk of an AMI in the next 12 months. This population will require new and unique interventions. And their identification will allow us to explore the most effective interventions to improve outcomes for this unique group,” said the CHIO.